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This repository is the official implementation of "RMNet: Equivalently Removing Residual Connection from Networks". Welcome to discuss this paper with me on知乎
Feb 18,2022, For better understanding, we implement a simpilify version RM Operation onResNet andMobileNetV2.
Jan 25,2022, RM+AMC purning:
https://github.com/fxmeng/RMNet/blob/aec110b528c2646a19a20777bd5b93500e9b74a3/RM+AMC/README.md
Dec 24, 2021, RMNet Pruning:
python train_pruning.py --sr xxx --threshold xxx
python train_pruning.py --eval xxx/ckpt.pth
python train_pruning.py --finetune xxx/ckpt.pth
Nov 15, 2021, RM Opeartion now supports PreActResNet.
Nov 13, 2021, RM Opeartion now supports SEBlock.
To install requirements:
pip install torchpip install torchvisionTo train the models in the paper, run this command:
python train.py -a rmrep_69 --dist-url 'tcp://127.0.0.1:23333' --dist-backend 'nccl' --multiprocessing-distributed --world-size 1 --rank 0 --workers 32 [imagenet-folder with train and val folders]To evaluate our pre-trained models trained on ImageNet, run:
python train.py -a rmrep_69 -e checkpoint/rmrep_69.pth.tar [imagenet-folder with train and val folders]Our model achieves the following performance on :
Help pruning achieve better performancedrive.google
| Method | Speed(Imgs/Sec) | Acc(%) |
|---|---|---|
| Baseline | 3752 | 71.79 |
| AMC(0.75) | 4873 | 70.94 |
| AMC(0.7) | 4949 | 70.84 |
| AMC(0.5) | 5483 | 68.89 |
| RM+AMC(0.75) | 5120 | 73.21 |
| RM+AMC(0.7) | 5238 | 72.63 |
| RM+AMC(0.6) | 5675 | 71.88 |
| RM+AMC(0.5) | 6250 | 71.01 |
| Arch | Top-1 Accuracy(%) | Top-5 Accuracy(%) | Train FLOPs(G) | Test FLOPs(M) |
|---|---|---|---|---|
| RepVGG-21 | 72.508 | 90.840 | 2.4 | 2.1 |
| RepVGG-21(RM 0.25) | 72.590 | 90.924 | 2.1 | 2.1 |
| RepVGG-37 | 74.408 | 91.900 | 4.4 | 4.0 |
| RepVGG-37(RM 0.25) | 74.478 | 91.892 | 3.9 | 4.0 |
| RepVGG-69 | 74.526 | 92.182 | 8.6 | 7.7 |
| RepVGG-69(RM 0.5) | 75.088 | 92.144 | 6.5 | 7.7 |
| RepVGG-133 | 70.912 | 89.788 | 16.8 | 15.1 |
| RepVGG-133(RM 0.75) | 74.560 | 92.000 | 10.6 | 15.1 |
Image Classification on ImageNetdrive.google.
| Model name | Top 1 Accuracy(%) | Top 5 Accuracy(%) |
|---|---|---|
| RMNeXt 41x5_16 | 78.498 | 94.086 |
| RMNeXt 50x5_32 | 79.076 | 94.444 |
| RMNeXt 50x6_32 | 79.57 | 94.644 |
| RMNeXt 101x6_16 | 80.07 | 94.918 |
| RMNeXt 152x6_32 | 80.356 | 80.356 |
If you find this code useful, please cite the following paper:
@misc{meng2021rmnet, title={RMNet: Equivalently Removing Residual Connection from Networks}, author={Fanxu Meng and Hao Cheng and Jiaxin Zhuang and Ke Li and Xing Sun}, year={2021}, eprint={2111.00687}, archivePrefix={arXiv}, primaryClass={cs.CV}}Our code is based onRepVGG andnni/amc pruning